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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Analytics & Insights | 10% | - Aggregation, filtering, and transformation functions - Correlation and pattern detection |
| Topic 2: Visualizing Metrics | 20% | - Using SignalFlow for custom queries - Creating and configuring charts, graphs, and time-series - Visualization best practices and effective communication |
| Topic 3: Dashboards & Operational Efficiency | 10% | - Reducing alert fatigue and optimizing monitoring - Designing clear, performant dashboards |
| Topic 4: Data Collection & Ingestion | 15% | - OpenTelemetry configuration and deployment - Integration with Kubernetes and cloud environments - Signal flow and data processing pipeline |
| Topic 5: Alerting with Detectors | 15% | - Threshold, anomaly, and multi-condition detectors - Common use cases: latency, saturation, errors - Notification rules and incident response |
| Topic 6: Built-in Monitoring Content | 10% | - Using pre-built dashboards and navigators - Content packs and quick-start monitoring |
| Topic 7: Metrics Concepts | 20% | - Dimensions, time-series data, and cardinality - Metric types: Gauges, Counters, Histograms, Summaries - Data retention and resolution settings |
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問題 #72
A customer has a large population of servers. They want to identify the servers where utilization has increased the most since last week. Which analytics function is needed to achieve this?
答案：A
解題說明：
The correct answer is C. Timeshift.
According to the Splunk Observability Cloud documentation1, timeshift is an analytic function that allows you to compare the current value of a metric with its value at a previous time interval, such as an hour ago or a week ago. You can use the timeshift function to measure the change in a metric over time and identify trends, anomalies, or patterns. For example, to identify the servers where utilization has increased the most since last week, you can use the following SignalFlow code:
timeshift(1w, counters("server.utilization"))
This will return the value of the server.utilization counter metric for each server one week ago. You can then subtract this value from the current value of the same metric to get the difference in utilization. You can also use a chart to visualize the results and sort them by the highest difference in utilization.
問題 #73
When metrics in a detector have different dimensions, a correlation conflict can occur, which prevents an alert from firing. Which of the choices below is the best method to resolve a correlation conflict?
答案：B
解題說明：
Applying an aggregate analytic function resolves correlation conflicts by summarizing metrics with different dimensions into a common value, allowing the detector to evaluate the alert condition across all relevant metrics consistently.
問題 #74
What are the best practices for creating detectors? (select all that apply)
答案：A,B,C,D
解題說明：
Explanation
The best practices for creating detectors are:
View data at highest resolution. This helps to avoid missing important signals or patterns in the data that could indicate anomalies or issues1 Have a consistent value. This means that the metric or dimension used for detection should have a clear and stable meaning across different sources, contexts, and time periods. For example, avoid using metrics that are affected by changes in configuration, sampling, or aggregation2 View detector in a chart. This helps to visualize the data and the detector logic, as well as to identify any false positives or negatives. It also allows to adjust the detector parameters and thresholds based on the data distribution and behavior3 Have a consistent type of measurement. This means that the metric or dimension used for detection should have the same unit and scale across different sources, contexts, and time periods. For example, avoid mixing bytes and bits, or seconds and milliseconds.
1: https://docs.splunk.com/Observability/gdi/metrics/detectors.html#Best-practices-for-detectors 2:
https://docs.splunk.com/Observability/gdi/metrics/detectors.html#Best-practices-for-detectors 3:
https://docs.splunk.com/Observability/gdi/metrics/detectors.html#View-detector-in-a-chart :
https://docs.splunk.com/Observability/gdi/metrics/detectors.html#Best-practices-for-detectors
問題 #75
Which of the following aggregate analytic functions will allow a user to see the highest or lowest n values of a metric?
答案：A
解題說明：
Explanation
The correct answer is D. Top / Bottom.
Top and bottom are aggregate analytic functions that allow a user to see the highest or lowest n values of a metric. They can be used to select a subset of the time series in the plot by count or by percent. For example, top (5) will show the five time series with the highest values in each time period, while bottom (10%) will show the 10% of time series with the lowest values in each time period1 To learn more about how to use top and bottom functions in Splunk Observability Cloud, you can refer to this documentation1.
問題 #76
What are the best practices for creating detectors? (select all that apply)
答案：A,B,C,D
問題 #77
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